{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V0</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V28</th>\n",
       "      <th>V29</th>\n",
       "      <th>V30</th>\n",
       "      <th>V31</th>\n",
       "      <th>V32</th>\n",
       "      <th>V33</th>\n",
       "      <th>V34</th>\n",
       "      <th>V35</th>\n",
       "      <th>V36</th>\n",
       "      <th>V37</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0.016</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.452</td>\n",
       "      <td>-0.901</td>\n",
       "      <td>-1.812</td>\n",
       "      <td>-2.360</td>\n",
       "      <td>-0.436</td>\n",
       "      <td>-2.114</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.450</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.109</td>\n",
       "      <td>-0.615</td>\n",
       "      <td>0.327</td>\n",
       "      <td>-4.627</td>\n",
       "      <td>-4.789</td>\n",
       "      <td>-5.101</td>\n",
       "      <td>-2.608</td>\n",
       "      <td>-3.508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.194</td>\n",
       "      <td>-0.893</td>\n",
       "      <td>-1.566</td>\n",
       "      <td>-2.360</td>\n",
       "      <td>0.332</td>\n",
       "      <td>-2.114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.671</td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.600</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.364</td>\n",
       "      <td>-0.335</td>\n",
       "      <td>-0.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.013</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.112</td>\n",
       "      <td>-0.797</td>\n",
       "      <td>-1.367</td>\n",
       "      <td>-2.360</td>\n",
       "      <td>0.396</td>\n",
       "      <td>-2.114</td>\n",
       "      <td>...</td>\n",
       "      <td>1.287</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.277</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.765</td>\n",
       "      <td>-0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.733</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.599</td>\n",
       "      <td>-0.679</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>-2.086</td>\n",
       "      <td>0.403</td>\n",
       "      <td>-2.114</td>\n",
       "      <td>...</td>\n",
       "      <td>1.298</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.603</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.684</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.337</td>\n",
       "      <td>-0.454</td>\n",
       "      <td>-1.073</td>\n",
       "      <td>-2.086</td>\n",
       "      <td>0.314</td>\n",
       "      <td>-2.114</td>\n",
       "      <td>...</td>\n",
       "      <td>1.289</td>\n",
       "      <td>0.183</td>\n",
       "      <td>1.078</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.418</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>0.364</td>\n",
       "      <td>-0.280</td>\n",
       "      <td>-0.028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      V0     V1     V2     V3     V4     V5     V6     V7     V8     V9  \\\n",
       "0  0.566  0.016 -0.143  0.407  0.452 -0.901 -1.812 -2.360 -0.436 -2.114   \n",
       "1  0.968  0.437  0.066  0.566  0.194 -0.893 -1.566 -2.360  0.332 -2.114   \n",
       "2  1.013  0.568  0.235  0.370  0.112 -0.797 -1.367 -2.360  0.396 -2.114   \n",
       "3  0.733  0.368  0.283  0.165  0.599 -0.679 -1.200 -2.086  0.403 -2.114   \n",
       "4  0.684  0.638  0.260  0.209  0.337 -0.454 -1.073 -2.086  0.314 -2.114   \n",
       "\n",
       "   ...      V28    V29    V30    V31    V32    V33    V34    V35    V36    V37  \n",
       "0  ...   -0.450  0.136  0.109 -0.615  0.327 -4.627 -4.789 -5.101 -2.608 -3.508  \n",
       "1  ...    0.671 -0.128  0.124  0.032  0.600 -0.843  0.160  0.364 -0.335 -0.730  \n",
       "2  ...    1.287 -0.009  0.361  0.277 -0.116 -0.843  0.160  0.364  0.765 -0.589  \n",
       "3  ...    1.298  0.015  0.417  0.279  0.603 -0.843 -0.065  0.364  0.333 -0.112  \n",
       "4  ...    1.289  0.183  1.078  0.328  0.418 -0.843 -0.215  0.364 -0.280 -0.028  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "from  pathlib import Path\n",
    "train_file=r\"zhengqi_train.txt\"\n",
    "test_file=r\"zhengqi_test.txt\"\n",
    "data_dir=r\"D:\\project\\tianchi\\steam_prodict\"\n",
    "\"\"\"Path(data_dir,train_file)\"\"\"\n",
    "table= pd.read_table(Path(data_dir,train_file)).iloc[:,:38]\n",
    "\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24947d1e2b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "v0_tar=table[[\"V0\",\"target\"]]\n",
    "v0_tar.head()\n",
    "v0_tar.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential() #线性核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "from keras import layers\n",
    "model.add(layers.Dense(1,input_dim=38))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "2888/2888 [==============================] - 1s 227us/step - loss: 2159508.3819\n",
      "Epoch 2/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1755946.5405\n",
      "Epoch 3/100\n",
      "2888/2888 [==============================] - 0s 39us/step - loss: 1412127.3400\n",
      "Epoch 4/100\n",
      "2888/2888 [==============================] - 0s 38us/step - loss: 1123304.0019\n",
      "Epoch 5/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 882135.2952\n",
      "Epoch 6/100\n",
      "2888/2888 [==============================] - 0s 29us/step - loss: 683070.9254\n",
      "Epoch 7/100\n",
      "2888/2888 [==============================] - 0s 37us/step - loss: 520876.7387\n",
      "Epoch 8/100\n",
      "2888/2888 [==============================] - 0s 32us/step - loss: 390748.8205\n",
      "Epoch 9/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 288331.2504\n",
      "Epoch 10/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 208718.6739\n",
      "Epoch 11/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 148092.0714\n",
      "Epoch 12/100\n",
      "2888/2888 [==============================] - 0s 43us/step - loss: 102891.1865\n",
      "Epoch 13/100\n",
      "2888/2888 [==============================] - 0s 36us/step - loss: 69817.6726\n",
      "Epoch 14/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 46358.9481\n",
      "Epoch 15/100\n",
      "2888/2888 [==============================] - 0s 37us/step - loss: 29895.1716\n",
      "Epoch 16/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 18816.3328\n",
      "Epoch 17/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 11521.4361\n",
      "Epoch 18/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 6844.9622\n",
      "Epoch 19/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 3942.9317\n",
      "Epoch 20/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 2199.1706\n",
      "Epoch 21/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1183.7420\n",
      "Epoch 22/100\n",
      "2888/2888 [==============================] - 0s 43us/step - loss: 616.9517\n",
      "Epoch 23/100\n",
      "2888/2888 [==============================] - 0s 50us/step - loss: 310.7088\n",
      "Epoch 24/100\n",
      "2888/2888 [==============================] - 0s 46us/step - loss: 150.8254\n",
      "Epoch 25/100\n",
      "2888/2888 [==============================] - 0s 49us/step - loss: 70.9404\n",
      "Epoch 26/100\n",
      "2888/2888 [==============================] - 0s 33us/step - loss: 32.3090\n",
      "Epoch 27/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 14.4996\n",
      "Epoch 28/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 6.6361\n",
      "Epoch 29/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 3.3524\n",
      "Epoch 30/100\n",
      "2888/2888 [==============================] - 0s 38us/step - loss: 2.0412\n",
      "Epoch 31/100\n",
      "2888/2888 [==============================] - 0s 37us/step - loss: 1.5423\n",
      "Epoch 32/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.3644\n",
      "Epoch 33/100\n",
      "2888/2888 [==============================] - 0s 42us/step - loss: 1.3043\n",
      "Epoch 34/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2847\n",
      "Epoch 35/100\n",
      "2888/2888 [==============================] - 0s 41us/step - loss: 1.2789\n",
      "Epoch 36/100\n",
      "2888/2888 [==============================] - 0s 44us/step - loss: 1.2770\n",
      "Epoch 37/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2766\n",
      "Epoch 38/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2764\n",
      "Epoch 39/100\n",
      "2888/2888 [==============================] - 0s 44us/step - loss: 1.2764\n",
      "Epoch 40/100\n",
      "2888/2888 [==============================] - 0s 32us/step - loss: 1.2761\n",
      "Epoch 41/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2760\n",
      "Epoch 42/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2760\n",
      "Epoch 43/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2758\n",
      "Epoch 44/100\n",
      "2888/2888 [==============================] - 0s 42us/step - loss: 1.2757\n",
      "Epoch 45/100\n",
      "2888/2888 [==============================] - 0s 38us/step - loss: 1.2756\n",
      "Epoch 46/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2755\n",
      "Epoch 47/100\n",
      "2888/2888 [==============================] - 0s 42us/step - loss: 1.2752\n",
      "Epoch 48/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2751\n",
      "Epoch 49/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2749\n",
      "Epoch 50/100\n",
      "2888/2888 [==============================] - 0s 29us/step - loss: 1.2748\n",
      "Epoch 51/100\n",
      "2888/2888 [==============================] - 0s 29us/step - loss: 1.2748\n",
      "Epoch 52/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2745\n",
      "Epoch 53/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2741\n",
      "Epoch 54/100\n",
      "2888/2888 [==============================] - 0s 41us/step - loss: 1.2741\n",
      "Epoch 55/100\n",
      "2888/2888 [==============================] - 0s 33us/step - loss: 1.2736\n",
      "Epoch 56/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2735\n",
      "Epoch 57/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2733\n",
      "Epoch 58/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2730\n",
      "Epoch 59/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2728\n",
      "Epoch 60/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2724\n",
      "Epoch 61/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2722\n",
      "Epoch 62/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2722\n",
      "Epoch 63/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2715\n",
      "Epoch 64/100\n",
      "2888/2888 [==============================] - 0s 39us/step - loss: 1.2714\n",
      "Epoch 65/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2711\n",
      "Epoch 66/100\n",
      "2888/2888 [==============================] - 0s 42us/step - loss: 1.2703\n",
      "Epoch 67/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2698\n",
      "Epoch 68/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2700\n",
      "Epoch 69/100\n",
      "2888/2888 [==============================] - 0s 44us/step - loss: 1.2689\n",
      "Epoch 70/100\n",
      "2888/2888 [==============================] - 0s 47us/step - loss: 1.2692\n",
      "Epoch 71/100\n",
      "2888/2888 [==============================] - 0s 48us/step - loss: 1.2687\n",
      "Epoch 72/100\n",
      "2888/2888 [==============================] - 0s 56us/step - loss: 1.2679\n",
      "Epoch 73/100\n",
      "2888/2888 [==============================] - ETA: 0s - loss: 1.251 - 0s 28us/step - loss: 1.2674\n",
      "Epoch 74/100\n",
      "2888/2888 [==============================] - 0s 30us/step - loss: 1.2670\n",
      "Epoch 75/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2667\n",
      "Epoch 76/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2665\n",
      "Epoch 77/100\n",
      "2888/2888 [==============================] - 0s 42us/step - loss: 1.2651\n",
      "Epoch 78/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2644\n",
      "Epoch 79/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2641\n",
      "Epoch 80/100\n",
      "2888/2888 [==============================] - 0s 46us/step - loss: 1.2629\n",
      "Epoch 81/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2621\n",
      "Epoch 82/100\n",
      "2888/2888 [==============================] - 0s 42us/step - loss: 1.2623\n",
      "Epoch 83/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2626\n",
      "Epoch 84/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2614\n",
      "Epoch 85/100\n",
      "2888/2888 [==============================] - 0s 29us/step - loss: 1.2600\n",
      "Epoch 86/100\n",
      "2888/2888 [==============================] - 0s 29us/step - loss: 1.2586\n",
      "Epoch 87/100\n",
      "2888/2888 [==============================] - 0s 37us/step - loss: 1.2572\n",
      "Epoch 88/100\n",
      "2888/2888 [==============================] - 0s 32us/step - loss: 1.2569\n",
      "Epoch 89/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2560\n",
      "Epoch 90/100\n",
      "2888/2888 [==============================] - 0s 38us/step - loss: 1.2540\n",
      "Epoch 91/100\n",
      "2888/2888 [==============================] - 0s 32us/step - loss: 1.2538\n",
      "Epoch 92/100\n",
      "2888/2888 [==============================] - 0s 43us/step - loss: 1.2527\n",
      "Epoch 93/100\n",
      "2888/2888 [==============================] - 0s 31us/step - loss: 1.2510\n",
      "Epoch 94/100\n",
      "2888/2888 [==============================] - 0s 45us/step - loss: 1.2525\n",
      "Epoch 95/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2483\n",
      "Epoch 96/100\n",
      "2888/2888 [==============================] - 0s 35us/step - loss: 1.2469\n",
      "Epoch 97/100\n",
      "2888/2888 [==============================] - 0s 38us/step - loss: 1.2468\n",
      "Epoch 98/100\n",
      "2888/2888 [==============================] - 0s 37us/step - loss: 1.2464\n",
      "Epoch 99/100\n",
      "2888/2888 [==============================] - 0s 42us/step - loss: 1.2434\n",
      "Epoch 100/100\n",
      "2888/2888 [==============================] - 0s 40us/step - loss: 1.2416\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x24948aade48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "model.compile(optimizer='adam',\n",
    "             loss='mse') #优化函数, mse 方差\n",
    "x=v0_tar.index\n",
    "y=v0_tar[[\"V0\"]]\n",
    "model.fit(x,y,epochs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24948c07d68>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x,y)\n",
    "\n",
    "plt.plot(x,model.predict(x),color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一元回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24003945b38>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import pandas as pd\n",
    "#原函数\n",
    "x=np.arange(1,11,1,dtype=int)\n",
    "y=1.9*x+3.3\n",
    "y=list ( map( lambda n:n+random.randint(-1,1),y))\n",
    "#加噪声\n",
    "y2=-2*x+1\n",
    "y2=list ( map( lambda n:n+random.randint(-1,1),y2))\n",
    "yout=np.add(y,y2)\n",
    "table=pd.DataFrame({0:y,1:y2,2:yout})\n",
    "table.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_8 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import layers\n",
    "import keras\n",
    "model = keras.Sequential() #线性核\n",
    "model.add(layers.Dense(1,input_dim=1))\n",
    "model.compile(optimizer='adam',\n",
    "             loss='mse') #优化函数, mse 方差\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "9/9 [==============================] - 0s 221us/step - loss: 2.3087\n",
      "Epoch 2/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.3079\n",
      "Epoch 3/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.3071\n",
      "Epoch 4/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.3063\n",
      "Epoch 5/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.3055\n",
      "Epoch 6/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.3048\n",
      "Epoch 7/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.3040\n",
      "Epoch 8/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.3032\n",
      "Epoch 9/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.3024\n",
      "Epoch 10/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.3017\n",
      "Epoch 11/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.3009\n",
      "Epoch 12/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.3002\n",
      "Epoch 13/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2994\n",
      "Epoch 14/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2986\n",
      "Epoch 15/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2979\n",
      "Epoch 16/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2971\n",
      "Epoch 17/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2964\n",
      "Epoch 18/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2956\n",
      "Epoch 19/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2949\n",
      "Epoch 20/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2942\n",
      "Epoch 21/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2934\n",
      "Epoch 22/2000\n",
      "9/9 [==============================] - 0s 333us/step - loss: 2.2927\n",
      "Epoch 23/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2919\n",
      "Epoch 24/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2912\n",
      "Epoch 25/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2905\n",
      "Epoch 26/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2898\n",
      "Epoch 27/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2890\n",
      "Epoch 28/2000\n",
      "9/9 [==============================] - 0s 333us/step - loss: 2.2883\n",
      "Epoch 29/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2876\n",
      "Epoch 30/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2869\n",
      "Epoch 31/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2862\n",
      "Epoch 32/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2855\n",
      "Epoch 33/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2847\n",
      "Epoch 34/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2840\n",
      "Epoch 35/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2833\n",
      "Epoch 36/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2826\n",
      "Epoch 37/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2819\n",
      "Epoch 38/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2812\n",
      "Epoch 39/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2805\n",
      "Epoch 40/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2798\n",
      "Epoch 41/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2791\n",
      "Epoch 42/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2785\n",
      "Epoch 43/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2778\n",
      "Epoch 44/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2771\n",
      "Epoch 45/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2764\n",
      "Epoch 46/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2757\n",
      "Epoch 47/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2750\n",
      "Epoch 48/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2744\n",
      "Epoch 49/2000\n",
      "9/9 [==============================] - 0s 112us/step - loss: 2.2737\n",
      "Epoch 50/2000\n",
      "9/9 [==============================] - 0s 223us/step - loss: 2.2730\n",
      "Epoch 51/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2724\n",
      "Epoch 52/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2717\n",
      "Epoch 53/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2710\n",
      "Epoch 54/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2704\n",
      "Epoch 55/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2697\n",
      "Epoch 56/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2690\n",
      "Epoch 57/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2684\n",
      "Epoch 58/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2677\n",
      "Epoch 59/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2671\n",
      "Epoch 60/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2664\n",
      "Epoch 61/2000\n",
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      "Epoch 62/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2651\n",
      "Epoch 63/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2645\n",
      "Epoch 64/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2638\n",
      "Epoch 65/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2632\n",
      "Epoch 66/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2626\n",
      "Epoch 67/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2619\n",
      "Epoch 68/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2613\n",
      "Epoch 69/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2606\n",
      "Epoch 70/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2600\n",
      "Epoch 71/2000\n",
      "9/9 [==============================] - 0s 223us/step - loss: 2.2594\n",
      "Epoch 72/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2588\n",
      "Epoch 73/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2581\n",
      "Epoch 74/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2575\n",
      "Epoch 75/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2569\n",
      "Epoch 76/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2563\n",
      "Epoch 77/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2556\n",
      "Epoch 78/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2550\n",
      "Epoch 79/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2544\n",
      "Epoch 80/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2538\n",
      "Epoch 81/2000\n",
      "9/9 [==============================] - 0s 334us/step - loss: 2.2532\n",
      "Epoch 82/2000\n",
      "9/9 [==============================] - 0s 110us/step - loss: 2.2526\n",
      "Epoch 83/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2520\n",
      "Epoch 84/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2514\n",
      "Epoch 85/2000\n",
      "9/9 [==============================] - 0s 221us/step - loss: 2.2508\n",
      "Epoch 86/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2502\n",
      "Epoch 87/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2496\n",
      "Epoch 88/2000\n",
      "9/9 [==============================] - 0s 112us/step - loss: 2.2490\n",
      "Epoch 89/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2484\n",
      "Epoch 90/2000\n",
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      "Epoch 91/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2472\n",
      "Epoch 92/2000\n",
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      "Epoch 93/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2460\n",
      "Epoch 94/2000\n",
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      "Epoch 95/2000\n",
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      "Epoch 96/2000\n",
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      "Epoch 97/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2436\n",
      "Epoch 98/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2431\n",
      "Epoch 99/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2425\n",
      "Epoch 100/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2419\n",
      "Epoch 101/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 222us/step - loss: 2.2413\n",
      "Epoch 102/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2408\n",
      "Epoch 103/2000\n",
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      "Epoch 104/2000\n",
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      "Epoch 105/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2390\n",
      "Epoch 106/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2385\n",
      "Epoch 107/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2379\n",
      "Epoch 108/2000\n",
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      "Epoch 109/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2368\n",
      "Epoch 110/2000\n",
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      "Epoch 111/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2356\n",
      "Epoch 112/2000\n",
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      "Epoch 113/2000\n",
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      "Epoch 114/2000\n",
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      "Epoch 115/2000\n",
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      "Epoch 116/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2329\n",
      "Epoch 117/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2323\n",
      "Epoch 118/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2318\n",
      "Epoch 119/2000\n",
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      "Epoch 120/2000\n",
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      "Epoch 121/2000\n",
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      "Epoch 122/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2296\n",
      "Epoch 123/2000\n",
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      "Epoch 124/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2285\n",
      "Epoch 125/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2279\n",
      "Epoch 126/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2274\n",
      "Epoch 127/2000\n",
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      "Epoch 128/2000\n",
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      "Epoch 129/2000\n",
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      "Epoch 130/2000\n",
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      "Epoch 131/2000\n",
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      "Epoch 132/2000\n",
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      "Epoch 133/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2237\n",
      "Epoch 134/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2231\n",
      "Epoch 135/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2226\n",
      "Epoch 136/2000\n",
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      "Epoch 137/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2215\n",
      "Epoch 138/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2210\n",
      "Epoch 139/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2205\n",
      "Epoch 140/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2200\n",
      "Epoch 141/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2195\n",
      "Epoch 142/2000\n",
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      "Epoch 143/2000\n",
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      "Epoch 144/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2179\n",
      "Epoch 145/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2174\n",
      "Epoch 146/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2169\n",
      "Epoch 147/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2164\n",
      "Epoch 148/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2159\n",
      "Epoch 149/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2154\n",
      "Epoch 150/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2148\n",
      "Epoch 151/2000\n",
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      "Epoch 152/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2138\n",
      "Epoch 153/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2133\n",
      "Epoch 154/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2128\n",
      "Epoch 155/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2123\n",
      "Epoch 156/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2118\n",
      "Epoch 157/2000\n",
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      "Epoch 158/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2108\n",
      "Epoch 159/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2103\n",
      "Epoch 160/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2098\n",
      "Epoch 161/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2093\n",
      "Epoch 162/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2088\n",
      "Epoch 163/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2083\n",
      "Epoch 164/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2079\n",
      "Epoch 165/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2074\n",
      "Epoch 166/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2069\n",
      "Epoch 167/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2064\n",
      "Epoch 168/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2059\n",
      "Epoch 169/2000\n",
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      "Epoch 170/2000\n",
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      "Epoch 171/2000\n",
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      "Epoch 172/2000\n",
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      "Epoch 173/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2035\n",
      "Epoch 174/2000\n",
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      "Epoch 175/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2025\n",
      "Epoch 176/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2020\n",
      "Epoch 177/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2016\n",
      "Epoch 178/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2011\n",
      "Epoch 179/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.2006\n",
      "Epoch 180/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.2001\n",
      "Epoch 181/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.1997\n",
      "Epoch 182/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.1992\n",
      "Epoch 183/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.1987\n",
      "Epoch 184/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1982\n",
      "Epoch 185/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1978\n",
      "Epoch 186/2000\n",
      "9/9 [==============================] - 0s 112us/step - loss: 2.1973\n",
      "Epoch 187/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1968\n",
      "Epoch 188/2000\n",
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      "Epoch 189/2000\n",
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      "Epoch 190/2000\n",
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      "Epoch 191/2000\n",
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      "Epoch 192/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1945\n",
      "Epoch 193/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.1940\n",
      "Epoch 194/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.1936\n",
      "Epoch 195/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1931\n",
      "Epoch 196/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1927\n",
      "Epoch 197/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1922\n",
      "Epoch 198/2000\n",
      "9/9 [==============================] - 0s 111us/step - loss: 2.1917\n",
      "Epoch 199/2000\n",
      "9/9 [==============================] - 0s 222us/step - loss: 2.1913\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 200/2000\n",
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     ]
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     ]
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     ]
    },
    {
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     "text": [
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     ]
    },
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      "Epoch 596/2000\n",
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     ]
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     ]
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     ]
    },
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     ]
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     ]
    },
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     "text": [
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     ]
    },
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     ]
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     ]
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     ]
    },
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     "text": [
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     ]
    },
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     "text": [
      "Epoch 1776/2000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1874/2000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 1972/2000\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x240014a4fd0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.fit(x,y,epochs=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24003163320>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,1.9*x+3.3)\n",
    "plt.plot(x,model.predict(x),color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_9 (Dense)              (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 3\n",
      "Trainable params: 3\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#二元线性回归\n",
    "model = keras.Sequential() #线性核\n",
    "model.add(layers.Dense(1,input_dim=2))\n",
    "model.compile(optimizer='adam',\n",
    "             loss='mse') #优化函数, mse 方差\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.2,   8.1],\n",
       "       [ 10. ,  10.9],\n",
       "       [ 11.8,  15.7],\n",
       "       [ 17.6,  17.5],\n",
       "       [ 20.4,  23.3],\n",
       "       [  0. ,  -3. ],\n",
       "       [ -5. ,  -8. ],\n",
       "       [ -9. , -10. ],\n",
       "       [-14. , -15. ],\n",
       "       [-16. , -18. ]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xin=np.array([y,y2])\n",
    "xin.shape\n",
    "xin=xin.reshape(10,2)\n",
    "xin\n",
    "# tmp=np.random.random((1000, 100))\n",
    "# len(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "10/10 [==============================] - 0s 698us/step - loss: 8.6934\n",
      "Epoch 2/2000\n",
      "10/10 [==============================] - 0s 499us/step - loss: 8.6861\n",
      "Epoch 3/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 8.6777\n",
      "Epoch 4/2000\n",
      "10/10 [==============================] - 0s 501us/step - loss: 8.6707\n",
      "Epoch 5/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 8.6620\n",
      "Epoch 6/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.6551\n",
      "Epoch 7/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.6480\n",
      "Epoch 8/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.6396\n",
      "Epoch 9/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.6313\n",
      "Epoch 10/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.6237\n",
      "Epoch 11/2000\n",
      "10/10 [==============================] - 0s 700us/step - loss: 8.6158\n",
      "Epoch 12/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 8.6076\n",
      "Epoch 13/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 8.6003\n",
      "Epoch 14/2000\n",
      "10/10 [==============================] - 0s 599us/step - loss: 8.5923\n",
      "Epoch 15/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.5849\n",
      "Epoch 16/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.5771\n",
      "Epoch 17/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.5701\n",
      "Epoch 18/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.5623\n",
      "Epoch 19/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.5541\n",
      "Epoch 20/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.5458\n",
      "Epoch 21/2000\n",
      "10/10 [==============================] - 0s 499us/step - loss: 8.5381\n",
      "Epoch 22/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.5306\n",
      "Epoch 23/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.5229\n",
      "Epoch 24/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.5151\n",
      "Epoch 25/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.5071\n",
      "Epoch 26/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.5000\n",
      "Epoch 27/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.4930\n",
      "Epoch 28/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4848\n",
      "Epoch 29/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4774\n",
      "Epoch 30/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4705\n",
      "Epoch 31/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4615\n",
      "Epoch 32/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4533\n",
      "Epoch 33/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4459\n",
      "Epoch 34/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4382\n",
      "Epoch 35/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4319\n",
      "Epoch 36/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4233\n",
      "Epoch 37/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.4157\n",
      "Epoch 38/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.4086\n",
      "Epoch 39/2000\n",
      "10/10 [==============================] - 0s 499us/step - loss: 8.3999\n",
      "Epoch 40/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.3932\n",
      "Epoch 41/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.3857\n",
      "Epoch 42/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.3774\n",
      "Epoch 43/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.3700\n",
      "Epoch 44/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.3629\n",
      "Epoch 45/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.3544\n",
      "Epoch 46/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.3472\n",
      "Epoch 47/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.3395\n",
      "Epoch 48/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.3328\n",
      "Epoch 49/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.3241\n",
      "Epoch 50/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.3167\n",
      "Epoch 51/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.3091\n",
      "Epoch 52/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.3012\n",
      "Epoch 53/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.2945\n",
      "Epoch 54/2000\n",
      "10/10 [==============================] - 0s 401us/step - loss: 8.2861\n",
      "Epoch 55/2000\n",
      "10/10 [==============================] - 0s 399us/step - loss: 8.2790\n",
      "Epoch 56/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.2724\n",
      "Epoch 57/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.2639\n",
      "Epoch 58/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.2558\n",
      "Epoch 59/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.2496\n",
      "Epoch 60/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.2413\n",
      "Epoch 61/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.2336\n",
      "Epoch 62/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.2259\n",
      "Epoch 63/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.2182\n",
      "Epoch 64/2000\n",
      "10/10 [==============================] - 0s 399us/step - loss: 8.2109\n",
      "Epoch 65/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.2028\n",
      "Epoch 66/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.1961\n",
      "Epoch 67/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.1889\n",
      "Epoch 68/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.1816\n",
      "Epoch 69/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.1730\n",
      "Epoch 70/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.1661\n",
      "Epoch 71/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.1586\n",
      "Epoch 72/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.1514\n",
      "Epoch 73/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.1429\n",
      "Epoch 74/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.1361\n",
      "Epoch 75/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.1287\n",
      "Epoch 76/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.1211\n",
      "Epoch 77/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.1148\n",
      "Epoch 78/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.1066\n",
      "Epoch 79/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.0988\n",
      "Epoch 80/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.0909\n",
      "Epoch 81/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.0836\n",
      "Epoch 82/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.0764\n",
      "Epoch 83/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.0698\n",
      "Epoch 84/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.0617\n",
      "Epoch 85/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.0537\n",
      "Epoch 86/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.0468\n",
      "Epoch 87/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.0390\n",
      "Epoch 88/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 8.0333\n",
      "Epoch 89/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 8.0238\n",
      "Epoch 90/2000\n",
      "10/10 [==============================] - 0s 399us/step - loss: 8.0167\n",
      "Epoch 91/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 8.0089\n",
      "Epoch 92/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 8.0019\n",
      "Epoch 93/2000\n",
      "10/10 [==============================] - 0s 399us/step - loss: 7.9949\n",
      "Epoch 94/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.9870\n",
      "Epoch 95/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.9806\n",
      "Epoch 96/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.9724\n",
      "Epoch 97/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.9655\n",
      "Epoch 98/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 400us/step - loss: 7.9579\n",
      "Epoch 99/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.9515\n",
      "Epoch 100/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.9429\n",
      "Epoch 101/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.9359\n",
      "Epoch 102/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.9281\n",
      "Epoch 103/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.9225\n",
      "Epoch 104/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.9140\n",
      "Epoch 105/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.9061\n",
      "Epoch 106/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.8984\n",
      "Epoch 107/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.8914\n",
      "Epoch 108/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.8843\n",
      "Epoch 109/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.8768\n",
      "Epoch 110/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.8695\n",
      "Epoch 111/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.8624\n",
      "Epoch 112/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.8550\n",
      "Epoch 113/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.8477\n",
      "Epoch 114/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.8395\n",
      "Epoch 115/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.8331\n",
      "Epoch 116/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.8250\n",
      "Epoch 117/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.8179\n",
      "Epoch 118/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.8105\n",
      "Epoch 119/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.8041\n",
      "Epoch 120/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.7960\n",
      "Epoch 121/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.7885\n",
      "Epoch 122/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.7833\n",
      "Epoch 123/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.7743\n",
      "Epoch 124/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.7664\n",
      "Epoch 125/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.7600\n",
      "Epoch 126/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.7528\n",
      "Epoch 127/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.7457\n",
      "Epoch 128/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.7378\n",
      "Epoch 129/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.7308\n",
      "Epoch 130/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.7235\n",
      "Epoch 131/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.7168\n",
      "Epoch 132/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.7103\n",
      "Epoch 133/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.7027\n",
      "Epoch 134/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.6945\n",
      "Epoch 135/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.6873\n",
      "Epoch 136/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.6801\n",
      "Epoch 137/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.6731\n",
      "Epoch 138/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.6657\n",
      "Epoch 139/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.6586\n",
      "Epoch 140/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.6512\n",
      "Epoch 141/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.6448\n",
      "Epoch 142/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.6382\n",
      "Epoch 143/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.6310\n",
      "Epoch 144/2000\n",
      "10/10 [==============================] - 0s 399us/step - loss: 7.6224\n",
      "Epoch 145/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.6154\n",
      "Epoch 146/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.6083\n",
      "Epoch 147/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.6015\n",
      "Epoch 148/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.5939\n",
      "Epoch 149/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.5870\n",
      "Epoch 150/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.5797\n",
      "Epoch 151/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.5721\n",
      "Epoch 152/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.5650\n",
      "Epoch 153/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.5579\n",
      "Epoch 154/2000\n",
      "10/10 [==============================] - 0s 401us/step - loss: 7.5508\n",
      "Epoch 155/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.5434\n",
      "Epoch 156/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.5368\n",
      "Epoch 157/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.5298\n",
      "Epoch 158/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.5227\n",
      "Epoch 159/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.5159\n",
      "Epoch 160/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.5086\n",
      "Epoch 161/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.5012\n",
      "Epoch 162/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.4936\n",
      "Epoch 163/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.4870\n",
      "Epoch 164/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.4793\n",
      "Epoch 165/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.4730\n",
      "Epoch 166/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.4651\n",
      "Epoch 167/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.4587\n",
      "Epoch 168/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.4513\n",
      "Epoch 169/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.4454\n",
      "Epoch 170/2000\n",
      "10/10 [==============================] - 0s 501us/step - loss: 7.4371\n",
      "Epoch 171/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.4304\n",
      "Epoch 172/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.4233\n",
      "Epoch 173/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.4163\n",
      "Epoch 174/2000\n",
      "10/10 [==============================] - 0s 499us/step - loss: 7.4093\n",
      "Epoch 175/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.4025\n",
      "Epoch 176/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 7.3947\n",
      "Epoch 177/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.3872\n",
      "Epoch 178/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.3809\n",
      "Epoch 179/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 7.3735\n",
      "Epoch 180/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.3671\n",
      "Epoch 181/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.3590\n",
      "Epoch 182/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 7.3534\n",
      "Epoch 183/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.3451\n",
      "Epoch 184/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.3390\n",
      "Epoch 185/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 7.3318\n",
      "Epoch 186/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.3246\n",
      "Epoch 187/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.3178\n",
      "Epoch 188/2000\n",
      "10/10 [==============================] - 0s 600us/step - loss: 7.3103\n",
      "Epoch 189/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.3030\n",
      "Epoch 190/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.2971\n",
      "Epoch 191/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.2897\n",
      "Epoch 192/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.2826\n",
      "Epoch 193/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.2754\n",
      "Epoch 194/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 400us/step - loss: 7.2685\n",
      "Epoch 195/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.2638\n",
      "Epoch 196/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.2546\n",
      "Epoch 197/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.2481\n",
      "Epoch 198/2000\n",
      "10/10 [==============================] - 0s 500us/step - loss: 7.2415\n",
      "Epoch 199/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.2344\n",
      "Epoch 200/2000\n",
      "10/10 [==============================] - 0s 300us/step - loss: 7.2275\n",
      "Epoch 201/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.2202\n",
      "Epoch 202/2000\n",
      "10/10 [==============================] - 0s 401us/step - loss: 7.2128\n",
      "Epoch 203/2000\n",
      "10/10 [==============================] - 0s 400us/step - loss: 7.2070\n",
      "Epoch 204/2000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x24005297b00>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xin,yout,epochs=2000,batch_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24004ebceb8>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(x,y,label=\"y\")\n",
    "plt.plot(x,y2,label=\"y2\")\n",
    "plt.plot(x,yout,label=\"yout\")\n",
    "#plt.plot(x,1.9*x+3.3)\n",
    "plt.plot(x,model.predict(xin),color=\"red\",label='perdict')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
